Junmei Ai

1.4k citations
20 papers · 765 · h-index 17

Impact in

    • MicroRNA in disease regulation
    • Cancer, Lipids, and Metabolism
    • Cancer-related molecular mechanisms research
    • Metabolomics and Mass Spectrometry Studies
    • Circular RNAs in diseases
    • RNA modifications and cancer

Papers in

    • Metabolomics and Mass Spectrometry Studies 4
    • Circular RNAs in diseases 2
    • Extracellular vesicles in disease 1
    • Sphingolipid Metabolism and Signaling 1
    • Gene expression and cancer classification 1
    • MicroRNA in disease regulation 4
    • Cancer-related molecular mechanisms research 4

Junmei Ai

20 papers receiving 753 citations

Peers

Junmei Ai
Comparison fields: 5 of 83
  • Cancer Research 330
  • Molecular Biology 516
  • Biochemistry 53
  • Spectroscopy 102
  • Horticulture 2
Replace Di Yu with:
Di Yu China
Erik Peter Germany
Alicja Pakiet Poland
Atsuki Ikeda Japan
Zhuozhong Wang China
Michaela Schwaiger-Haber United States
Chang Shao China
Kaushala S. Jayawardana Australia
Zixin Zhu China
Shicheng Fan China
Junmei Ai relative to Di Yu China Di Yu's profile →
Citations per field
00.5×10×20×30×40×50×
Di Yu · 1×
Citations per year

Countries citing papers authored by Junmei Ai

Since Specialization
Citations

This map shows the geographic impact of Junmei Ai's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Junmei Ai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junmei Ai more than expected).

Fields of papers citing papers by Junmei Ai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Junmei Ai. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Junmei Ai. The network helps show where Junmei Ai may publish in the future.

Co-authors

The 25 scholars most cited alongside Junmei Ai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Junmei Ai Line = papers co-authored together Junmei Ai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 2012169
2 201695
3 201661
4 201350
5 201949
6 201646
7 201745
8 201437
9 201131
10 201027
11 201322
12 201922
13 201821
14 201420
15 202019
16 201118
17 201818
18 201012
19 20142
20 20201

About Junmei Ai

Junmei Ai is a scholar working on Molecular Biology, Cancer Research, Pathology and Forensic Medicine, Oncology and Biochemistry, having authored 20 papers that have together received 765 indexed citations. Recurring topics across this work include MicroRNA in disease regulation (4 papers), Metabolomics and Mass Spectrometry Studies (4 papers), Cancer-related molecular mechanisms research (4 papers), Circular RNAs in diseases (2 papers), Extracellular vesicles in disease (1 paper), Sphingolipid Metabolism and Signaling (1 paper), Pancreatic and Hepatic Oncology Research (1 paper) and Gene expression and cancer classification (1 paper). The work is most often cited by research in Cancer Research (330 citations), Molecular Biology (516 citations), Biochemistry (53 citations), Spectroscopy (102 citations) and Horticulture (2 citations). Junmei Ai has collaborated with scholars based in United States, China and Italy. Frequent co-authors include Youping Deng, Jinghe Mao, Hankui Chen, Ruth Welti, Xinchun Zhou, Shengming Dai, Jeffrey R. Henegar, Steven A. Bigler, Mary R. Roth and Charles R. Pound. Their work appears in journals such as BMC Genomics, Oncotarget, PLoS ONE, BMC Systems Biology and Journal of Clinical Oncology.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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